Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography
Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical sce...
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sg-ntu-dr.10356-1465512023-12-29T06:46:34Z Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography Awasthi, Navchetan Jain, Gaurav Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. School of Chemical and Biomedical Engineering Engineering::Bioengineering Image Reconstruction Convolutional Neural Networks Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat. Accepted version 2021-03-01T01:44:06Z 2021-03-01T01:44:06Z 2020 Journal Article Awasthi, N., Jain, G., Kalva, S. K., Pramanik, M., & Yalavarthy, P. K. (2020). Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 67(12), 2660-2673. doi:10.1109/TUFFC.2020.2977210 1525-8955 0000-0001-8153-2786 0000-0002-9084-9395 0000-0003-1034-7246 0000-0003-2865-5714 0000-0003-4810-352X https://hdl.handle.net/10356/146551 10.1109/TUFFC.2020.2977210 32142429 2-s2.0-85091114233 12 67 2660 2673 en IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TUFFC.2020.2977210 application/pdf |
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Engineering::Bioengineering Image Reconstruction Convolutional Neural Networks Awasthi, Navchetan Jain, Gaurav Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography |
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Photoacoustic tomography (PAT) is a noninvasive imaging modality combining the benefits of optical contrast at ultrasonic resolution. Analytical reconstruction algorithms for photoacoustic (PA) signals require a large number of data points for accurate image reconstruction. However, in practical scenarios, data are collected using the limited number of transducers along with data being often corrupted with noise resulting in only qualitative images. Furthermore, the collected boundary data are band-limited due to limited bandwidth (BW) of the transducer, making the PA imaging with limited data being qualitative. In this work, a deep neural network-based model with loss function being scaled root-mean-squared error was proposed for super-resolution, denoising, as well as BW enhancement of the PA signals collected at the boundary of the domain. The proposed network has been compared with traditional as well as other popular deep-learning methods in numerical as well as experimental cases and is shown to improve the collected boundary data, in turn, providing superior quality reconstructed PA image. The improvement obtained in the Pearson correlation, structural similarity index metric, and root-mean-square error was as high as 35.62%, 33.81%, and 41.07%, respectively, for phantom cases and signal-to-noise ratio improvement in the reconstructed PA images was as high as 11.65 dB for in vivo cases compared with reconstructed image obtained using original limited BW data. Code is available at https://sites.google.com/site/sercmig/home/dnnpat. |
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School of Chemical and Biomedical Engineering |
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School of Chemical and Biomedical Engineering Awasthi, Navchetan Jain, Gaurav Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. |
format |
Article |
author |
Awasthi, Navchetan Jain, Gaurav Kalva, Sandeep Kumar Pramanik, Manojit Yalavarthy, Phaneendra K. |
author_sort |
Awasthi, Navchetan |
title |
Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography |
title_short |
Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography |
title_full |
Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography |
title_fullStr |
Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography |
title_full_unstemmed |
Deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography |
title_sort |
deep neural network-based sinogram super-resolution and bandwidth enhancement for limited-data photoacoustic tomography |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/146551 |
_version_ |
1787136480527777792 |